Deep learning: Taking two different paths

It is assumed incorrectly about the development of the AGI which is artificial general intelligence, which is the automation of self-awareness following the path having very smart machines and once the control of the machine of humans sentiment is created, it will get advanced towards the superintelligence. Present day learning technologies will give you the beginning and multiple intelligent machines will get it to development. And the other branch will mainly focus on automation which is more flexible and biologically stimulated.

The first path will show machines specialization which will help you to solve the particular problem. This kind of machine is AlphaGo. For solving the problem which is specific are highly engineered, doing it in a manner which is superhuman. It works as a game where advanced play progressed and was willing to reach a knowledge which is of a higher level. The intelligence path which is optimized works well the domains which are highly complex it will help to thrive in the problems relating to space and size. There are many new applications which are expected to combine the algorithms of computer science with deep learning to achieve the applications of narrow intelligence. It will have a major impact on self-driving cars and medical diagnosis. There is no need for AGI in this approach.

The other branch of development is the one which will mainly focus on the approaches which are inspired biologically and will be driven by the space of the applications of robots which may need adaptability of this kind to an environment. These systems are mainly used for interacting with humans. Among AGI that is artificial general intelligence, we have one common sentiment that mainly researches the themes of deep learning which seem to have missed the point. For AGI, deep learning is the best and effective starting point. For survival, there is no need for high-level intelligence. As you move forward with the increasing intelligence, it will be emerged by default this is the recent incorrect bias. There is no need for the high dimensional or complex inference required for these adaptable systems in the first branch.

The second branch will require driving its own function which is objective. Optimization is favored well by these systems and it serves as agents of connection to humans. Today this second branch does not exist to achieve a sentient intelligence one has to realize that it does not require a super intelligence or human intelligence. Only the ability to perform is needed to be observed the life of the biological forms just to realize that they are self-aware.

It is noted that in the leading scheme of multiple things, self-aware automation might come soon in future than anyone is expecting.